LangChain vs Llama Stack: Agent Frameworks Comparison
Compare LangChain and Llama Stack side by side. Both are tools in the Agent Frameworks category.
Quick Comparison
| Category | Agent Frameworks | Agent Frameworks |
| Pricing | Open Source | — |
| Best For | Developers building complex LLM applications who need a comprehensive orchestration framework | — |
| Website | langchain.com | github.com |
| Key Features |
| — |
| Use Cases |
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When to Choose LangChain vs Llama Stack
- Building RAG applications
- Multi-agent orchestration with LangGraph
- Tool-using conversational agents
How to Choose a Agent Frameworks Tool
Key criteria to evaluate when comparing Agent Frameworks solutions:
About LangChain
LangChain is the most widely adopted framework for building LLM-powered applications and AI agents. It provides abstractions for chains, agents, tools, memory, and retrieval that make it easy to compose complex AI systems. LangGraph, its agent orchestration layer, enables building stateful, multi-actor workflows with human-in-the-loop capabilities. LangSmith provides tracing, evaluation, and monitoring. The LangChain ecosystem is the largest in the AI application development space.
About Llama Stack
Llama Stack is Meta's standardized API and SDK for building AI applications on top of Llama models. It provides a unified interface for inference, safety, memory, and agentic workflows — with swappable providers for local, cloud, and on-device deployment. As the official framework for the Llama ecosystem, it is becoming the default for teams building on open-source Llama models.
What is Agent Frameworks?
Developer frameworks and SDKs for building autonomous AI agents with tool use, planning, multi-step reasoning, and orchestration capabilities.
Browse all Agent Frameworks tools →